A hybrid weight-based and string distances using particle swarm optimization for prioritizing test cases

Regression testing is concerned with testing the modified version of software. However, to re-test entire test cases require significant cost and time. To reduce the cost and time, higher average percentage fault detection (APFD) rate and faster execution to kill fault mutant are required. Therefore, to achieve these two requirements, an improvement to existing Test Case Prioritization (TCP) technique for a more effective regression testing is offered. A weight-hybrid string distance technique and prioritization using particle swarm optimization (PSO) is proposed. Distance between test cases and weight for each test case, and hybridization of both values for weight-hybrid string distance are calculated. This experiment was evaluated using Siemens dataset. Result obtained from this experiment shows that weight-hybrid string distance is capable of improving APFD values whereby APFD value for hybrid TFIDF-JC is equal to 97.37%, which shows the highest improvement by 4.74% as compared to non-hybrid JC. Meanwhile, for percentage of test cases needed to kill 100% fault mutants, hybrid TFIDF-M yields the lowest value, 22.88%, which shows a 76% improvement as compared to its non-hybrid string distance

Test case prioritization technique based on string distance metrics

Numerous test case prioritization (TCP) approaches have been introduced to enhance the test viability in software testing activity with the goal to maximize early average percentage fault detection (APFD). There are different approaches and the process for each approach varies. Furthermore, these approaches are not well documented within the single TCP approach. Based on current studies, having an approach that has high coverage effectiveness (CE) and APFD rate, remains a challenge in TCP. The string-based approach is known to have a single string distance based metric to differentiate test cases that can improve the CE results. However, to differentiate precisely the test cases, the string distances require enhancement. Therefore, a TCP technique based on string distance metric was developed to improve CE and APFD rate. In this research, to differentiate precisely the test cases and counter the string distances problem, an enhanced string distances based metric with a string weight based metric was introduced. Then, the metric was executed under designed process for string-based approach for complete evaluation. Experimental results showed that the enhanced string metric had the highest APFD with 98.56% and highest CE with 69.82% in Siemen dataset, cstcas. Besides, the technique yielded the highest APFD with 76.38% in Robotic Wheelchair System (RWS) case study. As a conclusion, the enhanced TCP technique with weight based metric has prioritised the test case based on their occurrences which helped to differentiate precisely the test cases, and improved the overall scores of APFD and CE